Wear mechanism of heavy load friction contact pairs in tracked vehicle by combined ferrography and surface analysis

Ferrography is deemed as one of the most effective methods for wear particle analysis and failure diagnosis. By analyzing the configuration, content and composition of wear particles in the lubricanting grease and the surface state of the worn surface with combined ferrography and surface analysis techniques, the wear mechanism of the ball groove of the master clutch's release device of a heavy load tracked vehicle was determined. Results show that the controlling wear mechanism is combined of abrasion, adhesion, contact fatigue and corrosion wear, which demonstrates the effectiveness of using combined ferrography and worn surface analysis for the study of wear mechanism of contact surface with friction.

Design of a Fault Diagnosis System for the Power Device Based on Ferrography and Image Recognition Technology

In this paper, the characters of the ferrography and image recognitiontechnology are analyzed. The fault diagnosis system for the power device based on the ferrographyand image recognition technology is designed. At the same time, the structure, the design andimplementing method, and the functions of each module of this system are described in detail.

Diagnosis System for Journal Bearings on the Basis of Spectrograph and Ferrography Analysis

A diagnosis system and a diagnosis example for journal bearings, based on the analysis with ferrography and spectrometric oil analysis is introduced. It is able to predict the failures of journal bearings on three levels. Also, the failure modes and diagnosis rules of journals are described.

Application of Ferrography to Fault Diagnosis of Hydraulic Systems

This paper deals with research on the successful use of ferrography as a wear measurement method for condition monitoring and fault diagnosis of hydraulic systems. The analysis program and progression is discussed, and a case study for condition monitoring and fault diagnosis of hydraulic systems by means of ferrography is also reviewed.

Intelligent Identification of Wear Mechanism via On-line Ferrograph Images

Condition based maintenance（CBM） issues a new challenge of real-time monitoring for machine health maintenance. Wear state monitoring becomes the bottle-neck of CBM due to the lack of on-line information acquiring means. The wear mechanism judgment with characteristic wear debris has been widely adopted in off-line wear analysis; however, on-line wear mechanism characterization remains a big problem. In this paper, the wear mechanism identification via on-line ferrograph images is studied. To obtain isolated wear debris in an on-line ferrograph image, the deposition mechanism of wear debris in on-line ferrograph sensor is studied. The study result shows wear debris chain is the main morphology due to local magnetic field around the deposited wear debris. Accordingly, an improved sampling route for on-line wear debris deposition is designed with focus on the self-adjustment deposition time. As a result, isolated wear debris can be obtained in an on-line image, which facilitates the feature extraction of characteristic wear debris. By referring to the knowledge of analytical ferrograph, four dimensionless morphological features, including equivalent dimension, length-width ratio, shape factor, and contour fractal dimension of characteristic wear debris are extracted for distinguishing four typical wear mechanisms including normal, cutting, fatigue, and severe sliding wear. Furthermore, a feed-forward neural network is adopted to construct an automatic wear mechanism identification model. By training with the samples from analytical ferrograph, the model might identify some typical characteristic wear debris in an on-line ferrograph image. This paper performs a meaningful exploratory for on-line wear mechanism analysis, and the obtained results will provide a feasible way for on-line wear state monitoring.

A Development of A Computer aided Ferrographic System

In this paper the authors suggest a computer aided system for processing data and images of ferrographic analysis by the authors. The system consists of seven modules and five databases. There is a typical wear particle library in the system. Its applications state that the analytical speed increases with this system and more information can be obtained by using this system.

Optimized Mask-RCNN model for particle chain segmentation based on improved online ferrograph sensor

Ferrograph-based wear debris analysis(WDA)provides significant information for wear fault analysis of mechanical equipment.After decades of offline application,this conventional technology is being driven by the online ferrograph sensor for real-time wear state monitoring.However,online ferrography has been greatly limited by the low imaging quality and segmentation accuracy of particle chains when analyzing degraded lubricant oils in practical applications.To address this issue,an integrated optimization method is developed that focuses on two aspects:the structural re-design of the online ferrograph sensor and the intelligent segmentation of particle chains.For enhancing the imaging quality of wear particles,the magnetic pole of the online ferrograph sensor is optimized to enable the imaging system directly observe wear particles without penetrating oils.Furthermore,a light source simulation model is established based on the light intensity distribution theory,and the LED installation parameters are determined for particle illumination uniformity in the online ferrograph sensor.On this basis,a Mask-RCNN-based segmentation model of particle chains is constructed by specifically establishing the region of interest(ROI)generation layer and the ROI align layer for the irregular particle morphology.With these measures,a new online ferrograph sensor is designed to enhance the image acquisition and information extraction of wear particles.For verification,the developed sensor is tested to collect particle images from different degraded oils,and the images are further handled with the Mask-RCNN-based model for particle feature extraction.Experimental results reveal that the optimized online ferrography can capture clear particle images even in highly-degraded lubricant oils,and the illumination uniformity reaches 90%in its imaging field.Most importantly,the statistical accuracy of wear particles has been improved from 67.2%to 94.1%.

New synthesizing feature parameter of wear particles image

This paper outlines the application of wavelet analysis method to computering wear par-ticles image processing and introduces the concept of grain parameter for wear particle imagebased on statistical feature parameters. The feature of wear particles image can be obtained fromthe wavelet decomposition and the statistics analysis. Test results showed that grain parametercan be used as a synthesizing feature parameter for wear particle image.